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Abstract
The presence of rain degrades the performance of sea surface parameter estimation using X-band marine radar. In this article, a novel scheme is proposed to improve wind measurement accuracy from rain-contaminated X-band marine radar data. After extracting texture features from each image pixel, the rain-contaminated regions with blurry wave signatures are first identified using a self-organizing map (SOM)-based clustering model. Then, a convolutional neural network used for image haze removal, i.e., DehazeNet is introduced and incorporated into the proposed scheme for correcting the influence of rain on radar images. In order to obtain wind direction information, curve fitting is conducted on the average azimuthal intensities of rain-corrected radar images. On the other hand, wind speed is estimated from rain-corrected images by training a support vector regression-based model. Experiments conducted using datasets from both shipborne and onshore marine radar show that compared to results obtained from images without rain correction, the proposed method achieves relatively high estimation accuracy by reducing measurement errors significantly.
Highlights
T HE real-time monitoring of sea surface wind information enhances the safety, performance, and efficiency of various weather-sensitive on- and off-shore activities, such as port operations, cargo shipping, and marine resource development
Compared to other AI models or previous proposed image dehazing models that are based on a single assumption (e.g., [26] and [25]), DehazeNet takes all of those assumptions into consideration in its feature extraction step, which improves the performance of the model in haze removal and rain correction
In order to detect the presence of rain, texture features extracted from radar data are input into an self-organizing map (SOM)-based model, which generates pixel-based rain identification results
Summary
T HE real-time monitoring of sea surface wind information enhances the safety, performance, and efficiency of various weather-sensitive on- and off-shore activities, such as port operations, cargo shipping, and marine resource development (e.g., offshore drilling and windfarming). CHEN et al.: A NOVEL SCHEME FOR EXTRACTING SEA SURFACE WIND INFORMATION rain-contaminated data with pixel-based rain correction techniques incorporated is worth pursuing. Following the common practice in radar data processing, the proposed scheme should consist of three major procedures: Detecting the presence of rain, mitigating the influence of rain, and estimating wind parameters from rain-corrected radar images. As for the third step, i.e., wind measurements, in the past few years, machine learning-based regression algorithms have been introduced to estimate wind speed and significant wave height from marine radar data [16], [19], [20] with higher accuracy and robustness in comparison with traditional methods, which can be incorporated into the proposed scheme. A novel scheme for wind parameter estimation using rain-contaminated X-band marine radar data based on the techniques mentioned above is presented.
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